Sequence-to-Sequence Similarity-Based Filter for Image Denoising

Image denoising has been a well-studied problem for imaging systems, especially imaging sensors. Despite remarkable progress in the quality of denoising algorithms, persistent challenges remain for a wide class of general images. In this paper, we present a new concept of sequence-to-sequence similarity (SSS). This similarity measure is an efficient method to evaluate the content similarity for images, especially for edge information. The approach differs from the traditional image processing techniques, which rely on pixel and block similarity. Based on this new concept, we introduce a new SSS-based filter for image denoising. The new SSS-based filter utilizes the edge information in the corrupted image to address image denoising problems. We demonstrate the filter by incorporating it into a new SSS-based image denoising algorithm to remove Gaussian noise. Experiments are performed over synthetic and experimental data. The performance of our methodology is experimentally verified on a variety of images and Gaussian noise levels. The results demonstrate that the proposed method's performance exceeds several current state-of-the-art works, which are evaluated both visually and quantitatively. The presented framework opens up new perspectives in the use of SSS methodologies for image processing applications to replace the traditional pixel-to-pixel similarity or block-to-block similarity.